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Large language models (LLMs) are employed to simulate human-like responses in social surveys, yet it remains unclear if they develop biases like social desirability response (SDR) bias. To investigate this, GPT-4 was assigned personas from four societies, using data from the 2022 Gallup World Poll. These synthetic samples were then prompted with or without a commitment statement intended to induce SDR. The results were mixed. While the commitment statement increased SDR index scores, suggesting SDR bias, it reduced civic engagement scores, indicating an opposite trend. Additional findings revealed demographic associations with SDR scores and showed that the commitment statement had limited impact on GPT-4's predictive performance. The study underscores potential avenues for using LLMs to investigate biases in both humans and LLMs themselves.

The prediction has served as a crucial scientific method in modern social studies. With the recent advancement of Large Language Models (LLMs), efforts have been made to leverage LLMs to predict the human features in social life, such as presidential voting. These works suggest that LLMs are capable of generating human-like responses. However, we find that the promising performance achieved by previous studies is because of the existence of input shortcut features to the response. In fact, by removing these shortcuts, the performance is reduced dramatically. To further revisit the ability of LLMs, we introduce a novel social prediction task, Soc-PRF Prediction, which utilizes general features as input and simulates real-world social study settings. With the comprehensive investigations on various LLMs, we reveal that LLMs cannot work as expected on social prediction when given general input features without shortcuts. We further investigate possible reasons for this phenomenon that suggest potential ways to enhance LLMs for social prediction.

Timestamps in digital traces include significant detailed information on when human behaviors occur, which is universally available and standardized in all types of digital traces. Nevertheless, the concept of time is under-explicated in empirical studies of online behaviors. This paper discusses the (un)desirable properties of timestamps in digital traces and summarizes how timestamps in digital traces have been utilized in existing studies of online behaviors. The paper argues that time-in-behaviors perspective can provide a microscope with a renovated temporal lens to observe and understand online behaviors. Going beyond the traditional behaviors-in-time perspective, time-in-behaviors perspective enables empirical examination of online behaviors from multiple units of analysis (e.g., discrete behaviors, behavioral sessions, and behavioral trajectories) and from multiple dimensions (e.g., duration, order, transition, rhythm). The paper shows the potentials of the time-in-behaviors perspective with several empirical cases and proposes future directions in explicating the concept of time in computational social science.

How to achieve academic career success has been a long-standing research question in social science research. With the growing availability of large-scale well-documented academic profiles and career trajectories, scholarly interest in career success has been reinvigorated, which has emerged to be an active research domain called the Science of Science (i.e., SciSci). In this study, we adopt an innovative dynamic perspective to examine how individual and social factors will influence career success over time. We propose ACSeeker, an interactive visual analytics approach to explore the potential factors of success and how the influence of multiple factors changes at different stages of academic careers. We first applied a Multi-factor Impact Analysis framework to estimate the effect of different factors on academic career success over time. We then developed a visual analytics system to understand the dynamic effects interactively. A novel timeline is designed to reveal and compare the factor impacts based on the whole population. A customized career line showing the individual career development is provided to allow a detailed inspection. To validate the effectiveness and usability of ACSeeker, we report two case studies and interviews with a social scientist and general researchers.

A new model, called "Human Dynamics", has been recently proposed that individuals execute activities based on a perceived priority of tasks, which can be characterized by a power-law distribution of waiting time between consecutive tasks (Barabasi, 2005). This power-law distribution has been found to exist in diverse human behaviors, such as mail correspondence, e-mail communication, webpage browsing, video-on-demand, and mobile phone calls. However, the pattern has been observed at the global (i.e., aggregated) level without considering individual differences. To guard against ecological fallacy, it is necessary to test the model at the individual level. The current study aims to address the following questions: Is the power-law uniform across individuals? What distribution do individual behaviors follow? We examine these questions with a client log file of nearly 4,000 Internet users' web browsing behavior and a server log file of 2,300,000 users' file-sharing behaviors in a P2P system. The results confirm the human dynamic model at the aggregate-level both in webpage browsing and P2P usage behavior. We have also found that there is detectable variability across the individuals in the decaying rate (i.e., the exponent gamma) of the power-law distribution, which follows well-known distributions (i.e., Gaussian, Weibull, and log-normal)., Comment: Paper presented at the Fifth Chinese Conference of Complex Networks (CCCN09), Qindao, China

Mobile phone use is an unfolding process by nature. In this study, it is explicated as two sequential processes: mobile sessions composed of an uninterrupted set of behaviors and mobile trajectories composed of mobile sessions and mobile-off time. A dataset of a five-month behavioral logfile of mobile application use by approximately 2,500 users in Hong Kong is used. Mobile sessions are constructed and mined to uncover sequential characteristics and patterns in mobile phone use. Mobile trajectories are analyzed to examine intraindividual change and interindividual differences on mobile re-engagement as indicators of behavioral dynamics in mobile phone use. The study provides empirical support for and expands the boundaries of existing theories about combinatorial use of information and communication technologies (ICTs). Finally, the understanding on mobile temporality is enhanced, that is, mobile temporality is homogeneous across social sectors. Furthermore, mobile phones redefine, rather than blur, the boundary between private and public time., Comment: 36 pages, 4 figures

Research on mobile phone use often starts with a question of "How much time users spend on using their phones?". The question involves an equal-length measure that captures the duration of mobile phone use but does not tackle the other temporal characteristics of user behavior, such as frequency, timing, and sequence. In the study, we proposed a variable-length measure called "session" to uncover the unmeasured temporal characteristics. We use an open source data to demonstrate how to quantify sessions, aggregate the sessions to higher units of analysis within and across users, evaluate the results, and apply the measure for theoretical or practical purposes., Comment: Preprint of forthcoming article in Mobile Media & Communication